{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### slim 下实现的densenet代码\n",
    "\n",
    "#### https://github.com/stoensin/simple_densenet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Contains a variant of the densenet model definition.\"\"\"\n",
    "\n",
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "slim = tf.contrib.slim\n",
    "\n",
    "\n",
    "def trunc_normal(stddev): return tf.truncated_normal_initializer(stddev=stddev)\n",
    "\n",
    "\n",
    "def bn_act_conv_drp(current, num_outputs, kernel_size, scope='block'):\n",
    "    current = slim.batch_norm(current, scope=scope + '_bn')\n",
    "    current = tf.nn.relu(current)\n",
    "    current = slim.conv2d(current, num_outputs, kernel_size, scope=scope + '_conv')\n",
    "    current = slim.dropout(current, scope=scope + '_dropout')\n",
    "    return current\n",
    "\n",
    "\n",
    "def block(net, layers, growth, scope='block'):\n",
    "    for idx in range(layers):\n",
    "        bottleneck = bn_act_conv_drp(net, 4 * growth, [1, 1],\n",
    "                                     scope=scope + '_conv1x1' + str(idx))\n",
    "        tmp = bn_act_conv_drp(bottleneck, growth, [3, 3],\n",
    "                              scope=scope + '_conv3x3' + str(idx))\n",
    "        net = tf.concat(axis=3, values=[net, tmp])\n",
    "    return net\n",
    "def transition_layer(net,in_size, scope):\n",
    "    \"\"\"\n",
    "    The transition layers used in our experiments consist of a batch normalization layer\n",
    "    and an 1×1 convolutional layer\n",
    "    followed by a 2×2 average pooling layer.\n",
    "    \"\"\"\n",
    "    net= slim.conv2d(net, in_size, [1,1], scope=scope + '_conv1x1')\n",
    "    net= slim.batch_norm(net, scope=scope + '_bn')\n",
    "    net= tf.nn.relu(net)\n",
    "    net = slim.dropout(net, keep_prob=0.5,scope=scope + '_dropout')\n",
    "    net= slim.avg_pool2d(net, [2, 2], stride=2, scope=scope+'_pool_2x2')\n",
    "\n",
    "    return net\n",
    "\n",
    "def densenet(images, num_classes=1001, is_training=False,\n",
    "             dropout_keep_prob=0.2,\n",
    "             scope='densenet'):\n",
    "    \"\"\"Creates a variant of the densenet model.\n",
    "\n",
    "      images: A batch of `Tensors` of size [batch_size, height, width, channels].\n",
    "      num_classes: the number of classes in the dataset.\n",
    "      is_training: specifies whether or not we're currently training the model.\n",
    "        This variable will determine the behaviour of the dropout layer.\n",
    "      dropout_keep_prob: the percentage of activation values that are retained.\n",
    "      prediction_fn: a function to get predictions out of logits.\n",
    "      scope: Optional variable_scope.\n",
    "\n",
    "    Returns:\n",
    "      logits: the pre-softmax activations, a tensor of size\n",
    "        [batch_size, `num_classes`]\n",
    "      end_points: a dictionary from components of the network to the corresponding\n",
    "        activation.\n",
    "    \"\"\"\n",
    "    growth = 24\n",
    "    compression_rate = 0.5\n",
    "\n",
    "    def reduce_dim(input_feature):\n",
    "        return int(int(input_feature.get_shape()[-1]) * compression_rate)\n",
    "\n",
    "    end_points = {}\n",
    "\n",
    "    with tf.variable_scope(scope, 'DenseNet', [images, num_classes]):\n",
    "        with slim.arg_scope(bn_drp_scope(is_training=is_training,keep_prob=dropout_keep_prob)) as ssc:\n",
    "            # pass\n",
    "            #images: A batch of `Tensors` of size [batch_size, height, width, channels].\n",
    "            ##########################\n",
    "            # network architecture work on cifar10 dataset:\n",
    "            # (batch, 112, 112, 48)  conv7x7\n",
    "            # (batch, 56, 56, 48)    max_pool3x3\n",
    "            # (batch, 56, 56, 192)   denseblock1\n",
    "            # (batch, 56, 56, 96)    transition conv1x1\n",
    "            # (batch, 28, 28, 96)    transition avg_pool2x2\n",
    "            # (batch, 28, 28, 384)   denseblock2\n",
    "            # (batch, 28, 28, 192)   transition conv1x1\n",
    "            # (batch, 14, 14, 192)   transition avg_pool2x2\n",
    "            # (batch, 14, 14, 768)   denseblock3\n",
    "            # (batch, 14, 14, 384)   transition conv1x1\n",
    "            # (batch, 7, 7, 384)     transition avg_pool2x2\n",
    "            # (batch, 7, 7, 768)     denseblock4\n",
    "            # (batch, 768)           global average pool7x7\n",
    "            # (batch, 10)            classifier-layer\n",
    "\n",
    "            # 从网络结构来看,网络变得越来越窄,而这样越来越窄的效果就是 densenet 结构设计的精妙之处.\n",
    "\n",
    "            # code start\n",
    "\n",
    "            #  The initial convolution layer comprises 2k(k:growth) convolutions of size 7×7 with stride 2\n",
    "            #  Before entering the first dense block, a convolution with 16 (or twice the growth rate for DenseNet-BC) output channels is performed on the input images\n",
    "            net= slim.conv2d(images, growth*2, [7,7],stride=2, scope=scope + '_conv7x7')\n",
    "            end_points[scope + '_conv7x7'] = net\n",
    "\n",
    "            net= slim.max_pool2d(net, [3,3],stride=2,padding='SAME', scope=scope + '_pool3x3')\n",
    "            end_points[scope + '_max_pool3x3'] = net\n",
    "\n",
    "           # denseblock1:\n",
    "           # denseblock的输出H*W维度不会变化,channel会根据growth*num增长\n",
    "           # 这里的 growth 就起到一个限制网络变宽,对参数保持一个限制,而又在深度上保持按比例的增长,其内部的bottleneck实现又对channel数量进行了降低,在网络结构中处处都对参数进行着控制,\n",
    "           # 而反应到特征学习上来说,就是 each layer 只学习少量特征,这就减少了参数量,降低了网络的对于计算的需求;\n",
    "           # 对于网络的目标而言,denseblock内的复合结构对于输入的特征学习的方式-特征复用,每一层网络的输出都会被用于后面各层网络的计算;\n",
    "           # 而在该结构内各层连接的数量有 (l(l+1))//2 个,如果以l=50计算,连接数就有1275了,所以就有密集连接的说法;\n",
    "            net= block(net, 6, growth, scope= scope + '_denseblock1')\n",
    "            end_points[scope +'denseblock1'] = net\n",
    "\n",
    "            # transition layer1\n",
    "            # : To further improve model compactness, we can reduce the number of feature-maps at transition layers\n",
    "            #   经过 denseblock对网络输出维度 channel 的提升后,transition 则起到降维(对 Feature Map 数量和尺寸)作用,减少网络的计算量\n",
    "            net= transition_layer(net, reduce_dim(net), scope=scope + '_Transition_Layer1')\n",
    "            end_points[scope +'Transition_Layer1'] = net\n",
    "\n",
    "            # denseblock2\n",
    "            net= block(net, 12, growth, scope= scope + '_denseblock2')\n",
    "            end_points[scope +'denseblock2'] = net\n",
    "\n",
    "            # transition layer2\n",
    "            net= transition_layer(net, reduce_dim(net), scope=scope + '_Transition_Layer2')\n",
    "            end_points[scope +'Transition_Layer2'] = net\n",
    "\n",
    "            # denseblock3\n",
    "            net= block(net, 24, growth, scope=scope + '_denseblock3')\n",
    "            end_points[scope +'denseblock3'] = net\n",
    "\n",
    "            # transition layer3\n",
    "            net= transition_layer(net, reduce_dim(net), scope=scope + '_Transition_Layer3')\n",
    "            end_points[scope +'Transition_Layer3'] = net\n",
    "\n",
    "            # denseblock4\n",
    "            net= block(net, 16, growth, scope=scope + '_denseblock4')\n",
    "            end_points[scope +'denseblock4'] = net\n",
    "\n",
    "            net= slim.batch_norm(net, scope=scope + '_bn')\n",
    "            net= tf.nn.relu(net)\n",
    "            # Global average pool:对网络传输过来的特征图各层都做avg_pool,且pool的siez就和特征图同大小\n",
    "            # 原理和用FC全连接层类似,但这个方式带来的参数量很小,降低计算量,还相当于对特征直接进行了粗粒度分类,\n",
    "            net= slim.avg_pool2d(net, net.shape[1:3], stride=1,padding='VALID', scope=scope + '_gap_pool7x7')\n",
    "            # net= slim.conv2d(net,num_classes,[1,1],activation_fn=tf.nn.relu,scope=scope + '_conv1x1')\n",
    "            # net= slim.flatten(net)\n",
    "            end_points[scope + '_gap_pool7x7'] = net\n",
    "\n",
    "            # softmax classifier\n",
    "            # logits = slim.fully_connected(net, num_classes, scope=scope + '_output', activation_fn=tf.nn.softmax)\n",
    "            net= slim.conv2d(net, num_classes, [1, 1], activation_fn=None,normalizer_fn=None, biases_initializer = tf.constant_initializer(0.1),scope='_classifier_conv1x1')\n",
    "            logits = tf.squeeze(net,scope=scope+'_logit')\n",
    "            end_points[scope + 'logits'] = logits\n",
    "\n",
    "            # code end\n",
    "            ##########################\n",
    "\n",
    "    return logits, end_points\n",
    "\n",
    "\n",
    "def bn_drp_scope(is_training=True, keep_prob=0.5):\n",
    "    keep_prob = keep_prob if is_training else 1\n",
    "    with slim.arg_scope(\n",
    "        [slim.batch_norm],\n",
    "            scale=True, is_training=is_training, updates_collections=None):\n",
    "        with slim.arg_scope(\n",
    "            [slim.dropout],\n",
    "                is_training=is_training, keep_prob=keep_prob) as bsc:\n",
    "            return bsc\n",
    "\n",
    "\n",
    "def densenet_arg_scope(weight_decay=0.004):\n",
    "    \"\"\"Defines the default densenet argument scope.\n",
    "\n",
    "    Args:\n",
    "      weight_decay: The weight decay to use for regularizing the model.\n",
    "\n",
    "    Returns:\n",
    "      An `arg_scope` to use for the inception v3 model.\n",
    "    \"\"\"\n",
    "    with slim.arg_scope(\n",
    "        [slim.conv2d],\n",
    "        weights_initializer=tf.contrib.layers.variance_scaling_initializer(\n",
    "            factor=2.0, mode='FAN_IN', uniform=False),\n",
    "        activation_fn=None, biases_initializer=None, padding='same',\n",
    "            stride=1) as sc:\n",
    "        return sc\n",
    "\n",
    "\n",
    "densenet.default_image_size = 224\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 在数据集上训练的结果如图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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20ygeQDmeJkJFqOf8Q2VykOzvGUnYlYZ65CPqrsGJPesXvs/tAeyyP+B88vMg\nchWqTRfBAyurD9pfocbAMvHwHtSR7P6wkd0LKT0Kle+7sbN4gPpLQ9DrlA9Oipinr5VLrKNSl0t/\nr37hPlxKPy9GxuciIpGWD5u+mpIaag5YoCzLtWBbSrqL6eMHLkcoeV1AHpUR8zwfFpKLUGNAIPia\nEJZkgWDZcWH9eACz9VMkotBtSUO7ehEPNaeFvut2vvv95OV9sVDwJ8fYxz1Yo5JI0j7iRfRSETL+\nwDw2fSwQfANZrCVZKMkCgUAgEAgEgj8bFqskP7ZftxAIBAKBQCAQCJYLoSQLBAKBQCAQCARBCCVZ\nIBAIBAKBQCAIQijJAoFAIBAIBAJBEEJJFggEAoFAIBAIghDfSZ6L6UMPvoaDJh4l0ogF6/h9CI9C\nq40DYMw8gH0qDFDwpC7+4R2m8o3DhXXYRuTa+OX/NueUi+H+O7iVwIoYdLq45c3PacM8ZAclKFav\nR7tm6XLstNxFitQQHbXE769O2hm2eXzyJBAIBALB48pjakl20aHXo0sJ/CvBPOnCVJGPLiXff749\nMNxmQFfYjgRgaQ+6b/peOe7YtSZ0qUUkpRehSzHQZ134vHuAsRutXLpme/hVXSbGrtWT9EI5mQVG\nMo9+JLcNLm79toXsAiPZBdV84l7OErgw7ZvZD4caOnFOLWeeAUzeI/2lSs4PuRaO+1WZstFRV092\nQS3ZP+vxtvXyIf3HAG8eriW7oJb9nfceIAUX771YydamO0vPe7idzJcMvvHkw22jo6GVsWWVKYFA\nIBAIHh6PqZKsIq2qio5TelKBotfKMF04yJMKkD7zAB72H+3xR3ffh5H7AEhfyCcU1Z+qwHS+go7z\nFXSc38eTCmC8h637ethZZqC/10jNFht5OQ04F1Eix1Anh4YmHn5Vlwn7H2/CKwbMfWcxn8jxnqCk\nIv2gEfOFHYCK5TawSqOg26Xn6rtGLh/bweCZFjYF9ttyEp7A1fNV5OkewW6BIp69Z5swlcUBD/dk\nrLlQajI40d5EzQaIeaBOVPF8axVXF3MqYHDeuhxMF4zogk+sXDHB6TOdOB6kOAKBQCAQfA08pkoy\nRGg0aHXrUALqDetRa+Jk14Dpc0YHWjBZAq3AgcpJDLqn4lFr49Fq49FqNShXgPVGN5DMT3cloFTE\nkVmmB27S503H2tVE4Q4DJovf+ihZOslOyWf/cQ+crKVQn48uxciwz5Lmou+c0W8tPe23JI5daaKw\nsJbmaoMcrq/FPO4vs/N2O6U+S2s+79wIsFSPD9Los6Yb6LhtD6ifh75aA0fPdXKpukSOs6OeMTeM\nddWjS9GTfQY4U0uhXk/2vs5HXfMZAAAgAElEQVQZ1k3pi3kafc48XZgqSmi8EmxF99BXXUJdp0W+\ntHRTuqOExunr6Z54Yh3Ra+LQbcmhZk8YXLfJZRnpoVRfz/C05dHaTam+HqsbmLLRXFhC3ekmX/vU\ntQ36820op7S6ibpCOaywuj3AQi23Tba+nDcbWrk1GiAjIz2U6stpbqj17TAEtqtk6eHQdD9W13JI\nX77onQYAiftz/OrB3Fbvk4/S2iBr+pSD3tN++SmsaPWFmy/Wku39vePG7PZ/EMa66sneUcIbb7bQ\nMxAgUyHbHHBbaNTnU/izY7zzTk+AxViWD90LtZjxsD+nhOwdeo623V1UeWaMAX095vHp/OTyHKo2\netsgn3euWUKmJRAIBALBUnhslWQAptyyQhW4hesEtmRQsyuM0rfms0raaKyup7G2nsbaWhrP3QRA\n+s8JiI3z6dmsXkc68LlLVjik0X56R23YA0zLythkai4cpDgrDLJyOFBVQUdzLmqvBa+/oYS84/dp\nPm/kxoViONlE6TlZuXDY79A7MMCttVlcvVBG0dAAv2jzbnFPWXijoJVVB8u48b6RjmMZKF3Tio+d\nxueM1IVn0PGukeafhLG/oIw+nyHbw9iQjebjLXRFZdFx3kDND2NwTEH0phxM58soAnTe8taUJHst\nyaGYL08V6nUu6v41aGt+yk5vm4OYtTFy27nsmEYddI0EK4reznPb6L/igTVRKAHJZcM0dAe3VyGU\nJDumoTuyguie4NaAg8aTd9h9wcjlg4k0Vjd4t/g9jPVbMLX1EJNfgentHHrbWnnP51YRxndfLKZm\n7zNcujbApxN+ZVJy2TENWTjaH0fHu1XUZDnY//NuWcamLNS92MSlrFyuvl/F0/cGuDRk4VPngymj\n0zhvNJBdfZPyYxVcPV/MqostbHrzpjfUQ+/hEgpPTnDiVBVXLxhIwyIvFHBh/7cwit+u4Mbvatlf\nWk7/Q9jIiN6YQ80brxBzbZDz9wISDNnmgCKGnNcO8tPvR9Hc9hF2n6KvIiV/Hx2v56AjjOLX91L/\nKwO7Ny3CZ3niOpsKWmFPMVffreBI7E2yn2uSd3am5PJcapvgwAUjl19LoGpfOaYlLFoEAoFAIAjF\n460kz4czirR/LIBrZ+kbh0jFyllRVkVFseqvvH8R82yBT7mxBlzG6rIo35PB06sD4ofHoNXE8+Ra\nFbonNGg1GrQ62TKNe5DTZzzodm0mxj2B1RlJShb0/mYgwHKbxi9ffpZoTSI7yyIx/95rDZu6jx1Y\nRRiKiDi0W3LY+XcaOcxynTqg+fVctGviSHm5mCKgN9iauCWXE8Xb0GoTyHw1F204EBWHWpuAdgPE\nrF2PVhOPVhOzcJuGyPPJTZuhawAnHkyFeg512sBloZkwntbI7gzK2ESO7NlGcWJUQNtB73GvlTTV\nwKGhOC6/keENlNvY7y0QcO19mfBA/UFSNHHoMrNIx8HwqFcRngR2lbJ7UzzqjRnUbIC+Ib9VNEKt\nQZuUQPqcFQ2j5Y1ctGs0ZO7eAaODWN0g3fsDzUTSUZZB9GoNO18vXrjNFsRDX9NNyCpm95Z4orXP\n8lNjIlzsYWwKcN/hfBeUN1eQ+pSGaE0CuyvLvK4MKlLLSkl5wo112A54GB55CP7Vq+PQ6hJl+Qj8\nfaE2R0W0Nh5dgiyjgV4eEd4xEYOKJzfEo9YmoF7Ey4TWng+AZH756rNEr4ln58F8oIdbIx5fefYe\nK5PLk1FMzQZouLJ0P2qBQCAQCObiT/PrFpP3iVidQfP2BvJ+fZ3LCcFKcCQ79+jRLvTlhhUK1AGX\nERu3sXvj0otjHuiheUQuwypVAkUZ01a0+xAb6VMoZDeD+0iAUpHAgdeSyTxspLlaLnPNqQoyn4pB\n+uI+EEeMz+Qdg3YDnLZOAN60JyF1q2aeEk1b2xb/FlWoPA88mwycwmq9w3sDYGKAolg7kIZ6Ov7q\nBHa+mjAz0UnQ7cql/sX1fNJ1isKT95GmLZDu0BZBCfyLG++OghSgI6Y+Ma2EeXcBnEEW7OldiFmo\niFBO1xlgAma4P3j/jYgkNWQJF4MHaRJS4/3qaPRfrwdM2N0Q7bLTSxjFcyqUdt7Rl1E1BOlb5HZd\neDdg8eWai4XaHEK46kwF/bsIJPcEbEj0K9wR3yEdsI57YLVcnpg10wtgD/YhiNm++PQFAoFAIAjF\nY64kK2TFYI6XkyQgZU8u5DRwPhGISpwZwQ0EvVykVETBqA03XoVj/B4mYLtKVgwkyyC3/t2F+qlk\n1FHMwvxZ0GtJU3I5jrxWxU7t0l/Y0maUYs4AadyCqa6c/T/vJr09F9mqasct4a37BPYhSNg522L+\nICiV3nRmLCJC5BkRxYFYB2++eRblrgz2DvXQeNKG7pUyv+uK00L/gA3l2gR0mkhfqjFPrEetjkf9\nagVHrhSRW92N+dg2lGvWAR/5yvDwFMCAei4ptldxnG4Tl4NeYCk6mfIvo+bMt3fMr8A7P7sHRBGx\nAlDFkIqH4XEPSUGfYpOGu6kaCuOyqQldBBSm6IOU/jCUsTDn4FhGQrepA7uEvKOxKFZC4NdHXBPy\nePQq6krgc59HiIqYDWAPWPeN3Wil9V8sqLfmkrlFfJJOIBAIBEvjsXW3cFptWIfvIQHWoXuMWWx+\nK+Q06m3Ub4FLA8xSiOdCvWkbcJOGtkFw2+loOAskk6KRH8rWqw3k7aun1zrHtrZyJbT1YB5x4Jzw\nhoevI2cLHPqnBobHXTDpYux2Nx2L+VSc20Zv53XGJlwoo2KIXhPGtMuBct0z5OHhF+d6kKbAeqWd\no8D2TfNZjpdIrIZUHJiuDOIctyO5F8ozkpSMMHqv2Uj7YRbpW+1cGoDnN63zJSlZPyJ3XwO/+JdP\ngyvq/VdF5oEMuHZW9itVRcpl+MgCbhuXjrYDD2cRIDldSOMuJMA+PoE06ZotO0Eo1z7DThwcberB\nOWnH9D8alpyvekMCjHbSa7bhHHcAKlJeToAzp+izuGDSQuuRm7AlTfZpD1/H9g1w6PBZhidcMGmn\nv60d6yQolVGAB+uoQ365b1ZuYTyZFEnv8Q8YHrHjHF+cK4bkdCFNTvD5BPR+5kByu5CCP+c2F1Mu\npEkXYxMuwIV93IXkDMxTXtB29AzidDqQJhf2HdY+mwZ0884VC0y56Gs5AySQovbuygBHD7dgnQTn\n7Tb2D8FLz67z3e8Yukld2wDv3Xt8vjojEAgEgm8Oj6kl2UXXjwwcGpWveg8baSSSyx++gTIC/Naz\nMNJLcuFai+835bcVzKtsrUnDVDlAeoXfxaH5QgHR0+HKKMDBqjncNLSZBex9r5LsF0qASC5/+Ba6\ncBXprxs58nMDmc/d9MUtqqySk1NEQaBFWrEy4NpF3+EGCn2BkdQ0p8uWuhUaiptz6ctrIulMk5zm\nawZS1wSktcCiQBkqXJHAgco0MiuM1AHlpxrZ/VToPNUbN8OZeyStVaElA4738LQ2MiBNWbHxuV/M\nUUblUz/gyIZOSk9+hLlyM3mvxJFnKKcO0MWC/wslipn+sl4FTKmYp27hAYGWdpJebPUF9e4zyHVs\nbmS3ai7Z8F4r4ik/n0vpS01sOgOpW+IAGzHz+bPPhfYHnNg1QGGeAZ+MbC+m/uN/Ju/FIjlObCId\nr23z3qBi59sVfP7jSjL/fvol1ERMGYB6M/VZrZS+VOJLXhlkNNZmFXOgy0jmCzchNoP+9tzQll73\nXarSK7k0fX2xgaSLkLrHwImXQ7e5taOa9Gr/1yXycor8dQxH/gxeZTKZFUYuVXvTDHa/CUadQcfB\nQTIN5RwFIIz65mKiV+B32xjtIf17ctuk7yllZ+An/ZTeReVybEMIBAKB4E+eb3355ZdfdxmYdP/X\n11+IQCZdOCVQRKge3olzTtlyueRT/CZdSF/Mc9+UR7bWqSJnKUjLxiPOU3K64Nuq0Er918FIN7oX\n2uj48K0luA+EwOnCCUTMIx9yO4AyXDXrPunbYSjDF6GsOx043fP4oa9YSUTUIz5h0u3CGewr7kNB\nxOpIfzyXt22mx6N7kMJUI8+fbyRzLUhSGMqlLFgEAoFA8GdLuOIvvrWYeI+pJXmZCVcR8bCVsgjV\ng/nVhoe4b0UYyqjI+UKXh0ec5zfpaPDh0/lk/jaOAxlRvHdmALLyH46CDBChIiJE8LztsAS5Gr74\nz2SenM/NIYP+vgUszQ8ZabiVTXnd84SGcfnDJq8VWjX76PApsAOfT8rhj2yRKBAIBII/G4QlWSBY\nLOMW+j4eZGz0Pqs2JJG6Kf7rLtHSmPLI/uWzdkc8gOpRv+Mnu0y4XbBitgVY/sJLKMuwC3PXH1Bs\nSkM7x0u0AoFAIBDMx2ItyUJJFggEAoFAIBD82bBYJfmx/bqFQCAQCAQCgUCwXAglWSAQCAQCgUAg\nCEIoyQKBQCAQCAQCQRBCSRYIBAKBQCAQCIIQSrJAIBAIBAKBQBCE+E7yXLhdSNI36xu9y4E0YsE6\nfh/Co9Bq4wAYMw9gnwoDFDypi394h6kIFsCFddhG5Nr42d8EfphMuRjuv4NbCayIQaeLW8bMHl6e\nTstdpEgN0VFLPDBk0s6wzeOTb4FAIBAIFstjakl20aHXo0sJ/CvBPOnCVJGPLiWfjmGXL/ZwmwFd\nYTsSgKU96L7pe+W4Y9ea0KUWkZRehC7FQJ91vsMXZjJ2o5VL12wPv6rLxNi1epJeKCezwEjm0Y/k\ntsHFrd+2kF1gJLugmk/mOZzt4eCiY19AH+wox3T78Wm/h87kPdJfquT8kGvhuF+FKRsddfVkF9SS\n/bMeb78vM185TxfvvVjJ1qY7S75TGm4n8yWDb3z7cNvoaGhlbFllXCAQCASPM4+pkqwiraqKjlN6\nUoGi18owXTjIkwqQPvMAHvYf7fFHd9+HEfn4W+kLNxBG/akKTOcr6DhfQcf5fTypAMZ72Lqvh51l\nBvp7jdRssZGX04BzESVyDHVyaGji4Vd1mbD/8Sa8YsDcdxbziRzvSWsq0g8aMV/YAaiW/2yJUWCX\nnqutFdRneCgtMND4GC00HirhCVw9X0Webpl3LxTx7D3bhKksDnhExzh/5TxVPN9axdU965d8p1KX\ng+mCUT65L5AVE5w+04njAUojEAgEgj8PHlMlGSI0GrS6dSgB9Yb1qDVxsmvA9Nm+Ay2YLIFW4MCH\ncwy6p+JRa+PRauPRajUoV4D1RjeQzE93JaBUxJFZpgdu0udNx9rVROEOAyaL39onWTrJTsln/3EP\nnKylUJ+PLsXIsM9y5aLvnNFnMT102m9JG7vSRGFhLc3VBjlcX4t53F9m5+12Sn3W7nzeuRGgQI4P\n0uizphvouG0PqJ+HvloDR891cqm6xGuprWfMDWNd9ehS9GSfAc7UUqjXk72vc4Z1T/pinkafM08X\npooSGq8EK7ce+qpLqOu0yJeWbkp3lNA4fe0l9Yl1RKvjSS820pwFdUe7fYuSGfXX12Me9wZM2Wgu\nLKHudJMvvK5tMCDV+dt8Qaw9lOprMXW2UOi9v/GKTc5TH9D3bguNegO9Vpdc14ZySqubqCuU7yms\nbsc5JUcN3c9yX2Xry3mzoZVbowEyO9JDqb6c5oZa345HYD9Llh4OTddRX77oXQ8Aiftz/j5vm3sZ\n7jpLti+81hfu/LiV0h3+nYG6iwOLzjMUY131ZO8o4Y03W+gZCJDxhWTAbaFRn0/hz47xzjs9ARZj\nWV51L9RixsP+nBKyd+g52nZ34cLMO+bk/i/cZ+TQjmmZu/5orPQCgUAgWDYeWyUZgCm3/CAK3DJ1\nAlsyqNkVRulbPXPfh43G6noaa+tprK2l8dxNAKT/nIDYOJ+ezep1pAOfu2TlQxrtp3fUhj3AtKyM\nTabmwkGKs8IgK4cDVRV0NOei9pph+xtKyDt+n+bzRm5cKIaTTZSekx/mDvsdegcGuLU2i6sXyiga\nGuAXbd4t5SkLbxS0supgGTfeN9JxLAOla1oJstP4nJG68Aw63jXS/JMw9heU0eczZHsYG7LRfLyF\nrqgsOs4bqPlhDI4piN6Ug+l8GUWAzlvempJkryU5FPPlqUK9zkXdvwZthU/Z6W1zELM2Rm47lx3T\nqIOukWBFyd95Tz+/DUb/gHUSmLjOpoJW2FPM1XcrOBJ7k+znmmQF2j3BrQEHjSfvsPuCkcsHE2ms\nbvBtqYdq84WQJDumoQFKDw/w0tsGOo7lEON2gXuCviEbn0+vj6buc2toWhY8jPVbMLX1EJNfgent\nHHrbWnnP6zoRsp8J47svFlOz9xkuXRvg0wm/oiu57JiGLBztj6Pj3Spqshzs/3m3LPNTFupebOJS\nVi5X36/i0pCFT52LV5LnJFSbA2NdRjIrunm+0sDVd6s48Xcehke9dRx18HRxGVffr+XqqVwGa2tp\nvGGfP69FEr0xh5o3XiHm2iDn7wXs1CwgAyhiyHntID/9fhTNbR9hn5q+UUVK/j46Xs9BRxjFr++l\n/lcGdm9ayGc51JiT+7/32iAJh6rk+p9soK7rz3RXRCAQCP5EeLyV5PlwRpH2jwVw7Sx94xCpWDkr\nyqqoKFb9lfcvYp4t4Ck31oDLWF0W5XsyeHp1QPzwGLSaeJ5cq0L3hAatRoNWJ1umcQ9y+owH3a7N\nxLgnsDojScmC3t8MBFiZ0vjly88SrUlkZ1kk5t97La1T97EDqwhDERGHdksOO/9OI4dZrlMHNL+e\ni3ZNHCkvF1ME9N4IeihvyeVE8Ta02gQyX81FGw5ExaHWJqDdADFr16PVxKPVxCzcpiHyfHLTZuga\nwIkHU6GeQ502cFloJoynNbL7gDI2kSN7tlGcGDVvFspwOa4CsPZ8ACTzy1efJXpNPDsP5gM93Brx\ngPdlwgP1B0nRxKHLzCIdh6ywLarNF6bmQhWpGxPQbtnBzu3xsIKZC4ngFxongV2l7N4Uj3pjBjUb\noG8oUEmcp5+BCLUGbVIC6XOWJIyWN3LRrtGQuXsHjA5idYN07w80E0lHWQbRqzVLqNn8hGxzXPSd\nG0S3x0De9gSi12hIfdVAptc9RJ2RT96zMdjv2bAThRa4NfTVlWRWx6HVJcryGvh7KBkAQEW0Nh5d\ngtw2ga5DEd4xGoOKJzfEo9YmoF6zgJvLQmNuEthVzO5NGqKfyqCmLJLm4x8Ja7JAIBA8xvxpft1i\n8j4RqzNo3t5A3q+vczkhWAmOZOcePdqFvtywQoE64DJi4zZ2b1x6ccwDPTSPyGVYpUqgKGPaanUf\nYiN9D3AJYOQ+EqBUJHDgtWQyDxtprpbLXHOqgsynYpC+uA/EEeMzeceg3QCnrROAN+1JSN06n/I0\nbXFc/FtLofI88GwycAqr9Q7vDYCJAYpi7UAa6un4qxPY+WpC6DwmXf5SuSdgQ6JfuYn4DumAddwD\nq+W28i1uvDsKUsA7b/O3+WKIRBcbJDNTzFJ4gq3vqU9MK1renQfntNU8RD/70nfPo1CpiPBGkt1g\nJmAqINj7/9QQtVksUqg2XzOBdQie3/udOe/tbygh94wDXaKGlCegGUhfeHtikcxtIV9IBiCE69BU\n0L8LEHrMyQu/1P/uV+PdY47pnwUCgUDwmPLYWpKdVhvWYdmKY713jzGLDSnggScBKZVN1I80kH3Y\nAmuCEphDP9RuyYHRdtm30W2n40glJpJJ81rLhs/J/r3v3J7jCwTKlZhPtmMeceCc8IYr1vH8FmAi\njryDBzlSdZBX/yEZ7d+sX9i9wW3jU57h6u8aMf+vRppfcbH/57JvrXLdZvKwsb+pB2kKrFfOsn8I\nfpoRWgldLMq1CaTiwHR1EOe4Hcm9QJ4RiVx+xUV2jpG08410bG0l/cfdHHg7y+e6IplbZF/o0zPd\nHuyf2RkbuYupwUBSQTd7jx1EFw7aZ38AQ+28c8UCUy76muowkcD2hV5s+yptHorwdTwfC4c++APS\nlAfzb85i+irpBSA5XUjjsszYxyeQJl0zZHkulGuT2ImDo7/uwTlpp3eJeWr/z80w2snvzTac4/Lr\na6HbPI60n0Ry9MfV9A7bwe1i+Fo7fRYXTFloPeOgqLKCyycq2P1/e5VFaeE8F0JyupAmJ/h8Ano/\ncyC5XUjBX6qYiykX0qTLOxZd2MddSM7AcatAiYPmrkGcTgfSZGhXlQXHXDj01tbRZ3XB+CDnz0Dq\n9gSfzE3Lv07fIqzLAoFA8JjwmFqSXXT9yMChUfmq97CRRiK5/OEbKCPAv7kaRnpJLlxr8f2m/LYC\nmO1+AcCaNEyVA6RX+K23zRcKiJ4OV0YBDlbNYYHWZhaw971Ksl8oASK5/OFb6MJVpL9u5MjPDWQ+\nd9MXt6iySk5OETXT2qRYGXDtou9wA4W+wEhqmtPlh+4KDcXNufTlNZF0pklO8zUDqYELgeC3+YNQ\nhgpXJHCgMo3MCiN1QPmpRnY/FTpP9cbNcOYeSWtVaMmA4z08rY0MSFO2+PksywCxYL7YxNaLQKyG\nmreNZG70WnzVGXQcHCTTUM5RAMKoby4megUwpZi59Y4CJaBUAIRu84WYXz5UpBzYBvuaSLrYBF5L\ns9IrarPaM9wfGLKfLe0kvdjqC+rdZ5DbvLmR3aq5yuK9VsRTfj6X0pea2HRG/ilmPrehudD+gBO7\nBijMM+CT11BtDuhePsKJz6opfKnMl0z9hQxYoSHvYCKZFZU0Vsj3zWicUHmGkkP3XarSK7k0fX2x\ngaSLkLrHwImXQ8kAWDuqSa/2u7Tk5RTNzFMRz97KZDIrjFyq9qYZaqdjMWMOhzcfIDGDq7kB6Xnl\nn/BH9EURgUAgEHxlvvXll19+3WVg0v1fX38hApl04ZRAEaF6eIdpOF2yFXipB5RMupC+mOe+KY9s\nHVNFztJHlo1HnafbhdMFERGq2X7AC/GgbR6KUP3xNaJLKaHjw7dkv/OvykJtPl8bLPUQHrcLp3O+\nL14oiFgdOU/YMjFveQLKMqf8u+jQF/He8xWc2KXB6fTIbScQCASCbyThir/41mLiPaaW5GUmXEXE\nw1A2AolQPdh2f3iI+1aEoYx6xIrEo85ToXrwE+getM1DEao/HjHDp/PJ/G0cBzKiICvn4SjIsHCb\nz9cGCtWSFk7ScCub8rrnCQ3j8odNoS3ND5n5yxNQlnnkXxqCXqf8DfaIpVj0BQKBQPCNRViSBYLH\nlXELfR8PMjZ6n8yXc77u0iydKcDtghWzlUr55dVHrGzOU57FlGXs4x6sUUkkaR/xolUgEAgES2ax\nlmShJAsEAoFAIBAI/mxYrJL82H7dQiAQCAQCgUAgWC6EkiwQCAQCgUAgEAQhlGSBQCAQCAQCgSAI\noSQLBAKBQCAQCARBCCVZIBAIBAKBQCAIQijJc+EOPsL2TxNpxMLw7UGGvcd7A4yZBzDfHsR8++6C\nRyN/Y3HaMH88gPn2AMMjj7gfJ+0z2nP5cdGxLx/djqDjjr2HXkhzHL++IJMh5H/6MI1Z9wxSmKJH\nl6Ln0MW7D5CpQCAQCATfLB7Tw0TkE672DwX+Jh9Lba0uobQLas6/RaZWPvVquM1A5geb6T+xA6Wl\nHV3AMcD+e+XjaseuNbF1X4/39ziaW6tIUS/8vdaxG630uDezc0vcw6jgsjN2rZ6t+7zHNifuoP9E\nDkpc3PptC6VtNh7FYQ7ScDtJLwX1RWwG/e25X+nADuk/BnjzcAu9o6DbY+ByqOOGl8hC/SwNt5NZ\n0MPlD88+uoMwRj3cOJ/lbTMXpooiSrv8wTt/Ukr5y8lztqn5XAnZXen0n92B0tKJ7sWWgNAEmltL\nSVGrYMrGOz8yUDUwHRZGzdtV/mPEw9dT/34Fzc9VcuuBNHOBQCAQCL5ZPKZKsoq0qio6nHc4WnCW\nhNfK2JkQQ4wChj/zALD/aA+ZJzLk6O77MCIfNyt9IZ+KVX/qILpwvNa3MNQKYLyHrft62FlmoDwr\nCtPPDeTlNHCjr5SIBUrkGOrkkDvhsVGS7X+8Ca8YMBcHKpAq0g8aMe9uRfdiD8t/6vR9IJHLF3JQ\nfCH3G9+O/Mon2ik1GZxo30aHPp/3HnIlFupnpS4H04UM1I/wpDgg4BjkMNRbcmnJTUS7ZiWjtz8g\nc189T29sZKcu6KjkyUHePO7gwNtpcpv/ZRz1laU8/TfrUSrsdFVXkvejD+hvz0HpnsAekUxLcw7a\nNXCrpZ7CH1ej9S4uIQzlag3qDXDrkdZcIBAIBILl4bF1t4jQaNDq1qEE1BvWo9bEoVwBPm12oAWT\nxRNwR6A1OAbdU/GotfFotfFotRqUK8B6oxtI5qe7ElAq4sgs0wM36fOmY+1qonCHAZPFv90sWTrJ\nTsln/3EPnKylUJ+PLsXI8OR0DBd954zopreiT/f4tsXHrjRRWFhLc7VBDtfXYh73l9l5u51S7326\nlHzeuRGwjT8+SKN+OsxAx217QP089NUaOHquk0vVJXKcHfWMuWGsqx5dip7sM8CZWgr1erL3dc7Y\nqpe+mKfR58zThamihMYrwS4GHvqqS6jrtMiXlm5Kd5TQOH0N4AZiNeg0Gm8/xKPVxABgPl3OoXMD\nAenZuVRYwqXbDjn8Yi3ZKQFluTE7/7kYu9JEYXV3QB/UU1jtPYp4ykbHdF94+6N/RE5nwX52W2jU\n51P4s2O8804PYzOMqR7MbfW+dEtrO3FOBZQnhAwsnTB02zNI0sURERWJdksaecDwv0/MijnccZZe\n0sjZ6D0lbnUi6duTiV4TScTqeNK2J8DoXbms4QnsPVbqTTeO1OfTAAeD9wJdL75KuQUCgUAg+Gbx\nmFqSvUy5ZWUnUCFxAlsyqFnTTelbPZiPbZvjRhuN1fWy9RgPPJFG0cvJSP85AbGJfqvx6nWkA5+7\nPEAY0mg/vaMO0pz+lJSxydRcWM8n71RTSg4HdidwwIU3behvKCHvTBzN541899sW3nixgVJFDCde\nTsBhv/P/s3f/MW1deeL337OpbYWOA6tgqrBune2FSPZmBVXYyIlImNUAWqY0TIFMRBKRoYkKzCy0\nuxCWtIEoP3ZgkiA1tERJVS4AACAASURBVBUkCnHDU4KyhcxA+ZZRSKeB5mlYHqpiTb5YIvEo3nEd\nFcMKrxeztsv2+eMabMyv/GyHmfOSIuX6Xp9z7j03ysefe+459JkdqLYV8+nlCFp21fFmx215eMC0\njV8eaGPNoTIGkjWMWj5jyDMThDhpfLGWMwkZdP0qBefHDRQcKCP6N80Yo+RzGhtxYPqgle378ulq\nicXSa8Y1DdLmXHpaUmjfU0dvVi4Vu+OB+8neLl6ndr2H0t/epugHIdnVaSd9HS60L8lBr9fjpGfU\nhf3eJEUzxygVMNpNY4NLrt8zife5rRTtTEK7UUN7aRtFP0mQr6XtJofNLlq1kYAH538oKH63GqMU\niX2glezSKrS/aSIxaumzcNmH6LsZEbJ9m76bGnnp4WkPTgxcaSlFGwVfvFdF3svNDPTvR71MP6PU\nkHvkECkjHWQf/YyXXs8jOrDLPdBAds0gVaerSV/n5K09DWxGg6Usael74BF47cNYHC7sH3dgIhLT\nprDMt+8OpjoHRafDn5J4sH4+jHP8Npeqh9m+r5ToVfPLHxs2A7EkShHzdwqCIAjCn4AVm0lekjuK\nlJ8egBvN9I9DpPLpeYesiYpizV8G/qgXGXM87cMeshmjz6Lq1QxeWBty/GoNki6O55+NQP+MDkmn\nQ9LLmWl8w1y46Ee/cysa3wR2dyTGLOj7N3NI5jaFf927hWhdAjllkVj+PZBpnZ7ECaxBgVIdi5Sc\nS84PdPI+203OAKZf5CGti8W4t5gioC88m5qcx9niVCTJQOYreUirgahYtJIBaQNono1H0gWzt0ta\nos7nN2+Fq2bc+OkpzOdwtwM8NkwoeEEnB1GqmAROvJpKcUJ4FBsBKkXwr4GMvzrxR6Rho+d3gczx\nRx2QkBsIgiPYXlaK8RkfdqsTImIB//29pKeCOU8VQreVcRQcypP76u4EyufigSHsUyzdz4HGR0tx\n6A1yHwVHefjpbxqErGJ2J8cRLW3h9doE+KCXsdkXIxe5Bx6B83ed5JU2cLDDATFb+Zu1c/ePfXyR\ndhLYsyU8eHbQ/vN6Cqq76QHSt8XPL9x+jW3Vw+QcKUZ68mNyBEEQBOE7sbIzyYuZmkS9NgNTegMF\n793kiiE8CI4k59V8pAUyZHOsUqIN2VRvSmX3pgdvjsXci+me3IY1EQaKMmYCk0mIiZwNqLwA9ybl\nrKbSQMWRJDKP1mKqkdt86nw1mRs1eL+eBGLRzKYANUgb4IJ9AgiUPQXbt+kWadFMRvr+X7Baqs6K\nLUnAeez223xohh7MFMU4gRS0M8evNZAzLzvqh5gUil7JnV+hMo7dWVDQMUzBJh0fXvTzWv2WwE4n\nl/LLOD4CackGtHwJcH9jmb1L7BsfpPDFevpQkJMeD3eHAd0Djc2eP1TFj3cKtscFf4hE/3U80IPT\nB8ql7oEHqDecNqMSSwbgHuZkWi3lH/wdZ3fGBfY6aDlqI+dI8fwssTKOiv5mKgBrdy2ZBw6T2Pd2\nMBieGKQwtxn9vmJOZCx2fwmCIAjCyrfCg2SlHEgsEMV4AeOreZDbQEsCEJUw9wAfEPZylUoZBaMO\nfAQClPG7cjYtQg5wvbZhvviDB+3GJLQLPNa3fOWa+8G03I4TR46TIy0/Q0Y4KaMUSwZ4x230nKni\n4BvXSOvMQ858OvF5CZz7BM4RMOTMz5g/DJUqUM6cAGqJOtVRVMS4eOutZlQ7M3htpJfGcw70+8qC\nj/LdNobMDlTPGtDrIoPFjvoXDQhf+EkG7PmI/nQDJmLpSgwM3bBe4/iIgis9TejVAHewGo+Fxb8K\nVDEQfnPEbJAzowvVZ/3tRfpiUhnqzJf329po39U777h5/RxiscC2b2xy9u/ur+4CUahXLR2zPxbq\neCSgP2TGCfeNVhrR0fPDpV8yldbHAl/im8l4u4c5/A/1OHfu50rxlqW+KgiCIAgr3oodbuG2O7Bb\n7+IF7CN3GbM55s/rq02lPhnazcwLiBei3ZwKDNLQMQw+J10NzUASRp0c4No/baCgvJ4++wKP9VVP\nQ0cvlnsu3BOB/avXk5sMh/+pAeu4B6Y8jN26RteN+5hH1+egr/smYxMeVFEaotcpmBkWoFr/dxTg\n5833e/FOg/16JyeB9M2PKbMXo2M7LnquD+Med+L1LVdnJMYMBX03HKT8OIu0bU7azfDS5vWzRXrt\nn5FX3sCbH38ZVpmD39sd2G027DYbVptjNnBUSSm8ho2C8m70O7Nms5kqVRTgxz7qgmkX/XU19M07\nCQXPJ0bS985HWO85cY/LfaJUa2C0m36bB8bNnKlzQeAHj+r7UTDqxO4G3HcwlXQCYT88FupngGkP\n3ikPYxMewINzfGau4QiMew1w8bxc55SNthODkJwSHM/8OPlsdF3oxGJ34HW7sA500AIY1s6ch5OW\ncjNpZfvm1T92o42u68OMTXjwjt+hq7UX+CvUSmDqDmfSamlHR3VuPGM2G1arDfcUgiAIgvAnaYVm\nkj1c/Vklh0flrb6jtTQG5klWqSGYPVSQVpIHN1pnP1M9pWRe4DNjXQo9x8ykVQeHOJguH5h9AQtV\nFOBizQLDNKTMA7z24TGyXy4hOO9yBGm/qOXEG5Vkvjg4e2zRseNyccqo2QANAOXTIdse+o82UDi7\nM5JTpjQ5U7lKR7Epj/6CJhIvNsllHqlk+7qQspb5UaBaar/SQMWxFDKrazkDVJ1vZPfGpevUbtoK\nF++S+GwEEhnwTi8vSCEZY6Uc4GvnvCWmAMxk54bOYhGcsxpiSfvHWM684+CVH4U8CdBupT6rjdI9\nJfJ2TODHQ1jQJ2UVU3G1lsyXB4PzL0spVG3opmCX/PqgHmC1/EVtch5FMbVkpuXL+2Jm2hhS5oL9\nDPauGtJqgmOJC3KLgvvTi6n//J9n6yQmga4jqYE2L3UP3D+vz48qcI2t59o4eC44/3Tazjz+JV3+\nAeX+vJMzRHIlM25eGf/jsXGwujPkk1hOnd+PdhV4HUM0AmAjb1fl7BFVpkZ2z0wt55afLpD+4O0X\nBEEQhD823/vmm2++6zYw5fvf774RoaY8uL2gVEeEvJj1iNweeViB+gFnA5jy4P16ke/NrH4WETkv\nQHxivos6F+P24H1KgWr1gw9l8U54YJH+9bo9oIp4/Ofn9uAmdE7jx8FDV3kRB0fCFmHxefB6gaci\nQn4QubhkLKFtmQVWvFMeWOyeW8zUMIV/X0sfkFZWSf3Ox7eAiyAIgiA8TquVf/G9+zlOBMmC8Odk\nygOrxbRtgiAIwp+v+w2SV+yYZEEQHoIIkAVBEAThvoggWRAEQRAEQRDCiCBZEARBEARBEMKIIFkQ\nBEEQBEEQwoggWRAEQRAEQRDCiCBZEARBEARBEMKIIFkQBEEQBEEQwoggeSG+mSWF/7R579mw3hrG\nag0ukz1mMWO5NYzl1p35y3yvFG4Hls/NWG6Zsd77lvtxyjnnej55HrrK96Pf0Tq7nHewLQ95Hy/1\nvZnFZOZ9Z5hCYz56Yz6HP7jz4HUKgiAIwh+ZFbssdVd+EQdHQj+Tl6W215RQehVOtbxNpiTPCWvt\nqCTzo60Mnd2BytaJfldbWHnB5YXHbjSxrbw38HksprbjGLXLr+g2NtBGr28rOcmxj+MEn7ixG/Vs\nKw8slZ2wg6Gzuajw8MWvWyntcAAKrnzSFFge+snwWjtJ3BPWFzFhK8c9TLn/aeato630jYJ+mdXl\nHtRy/ey1dpJ5oJcrnzQ/0Ws3x6ifgZaskGvmZ+j9OvLeGQ5sx9L1SS3SAu2xvF9C9tU0hpp3oLJ1\no9/VGrLXgKmtFKM2AqYdXPpZJcdnVxBXcOrd42RuClyH1fHU/59qTC8e4wuv7wmcpCAIgiB8u1Zo\nkBxByvHjdLlvc/JAM4YjZeQYNGiUYP3KD8DBk71kns2QD/dNwr1JALxf+wAF9ecPoV9NIPumQKsE\nxnvZVt5LTlklVVlR9LxRSUFuAwP9paiXaZFrpJvDPsOKCZKdvxuEfZVYikMDyAjSDtVi2d2Gflcv\nT37V6UkggSuXc1F+LfcbT0U+UoAMoNJlcLYzla78/Xz4mE9iuX5W6XPpuZyB9tsKkANCl7oeuyoH\nyCdOV5OZEMnvzXeIXGh59alh3nrHRcW7KfI1/34s9cdKeeFv41EpnVytOUbBzz5iqDMXlW8CpzqJ\nVlMu0jr4orWewp/XIAV+XIIC1Vod2g3wxbdzyoIgCILwRK3Y4RZqnQ5Jvx4VoN0Qj1YXi2oVzEaz\n5lZ6bP6Qb4RmgzXoN8ahleKQpDgkSYdqFdgHrgFJvL7TgEoZS2ZZPjBIf6Ac+9UmCndU0mMLPm72\n2rrJNu7n4Dt+OFdHYf5+9MZarFMzR3jof78W/cyj6Au9s4/Fx643UVhYh6mmUt6fX4dlPNhm961O\nSgPf0xv3c2kg5DH++DCN+TP7Kum65Qw5Pz/9dZWcfL+b9poS+Zgd9Yz5YOxqPXpjPtkXgYt1FObn\nk13ePedRvffrRS76gnV66KkuofF6+BADP/01JZzptsmbtmuU7iihcWYbwAfE6NDrdIF+iEPSaQCw\nXKji8PvmkPKctBeW0H7LJe//oI5sY0hbBubXv5Cx600U1lwL6YN6CmuuyRvTDrpm+iLQH0P35HKW\n7Wefjcb8/RT+y2kuXeplbE4y1Y+lo3623NK6btzTIe1Z4h54cC6uVg+Tc+Q4OclxqCI06JO3EL3A\njwVrVzN9pJC7KVL+YG0CaelJRK+LRL02jpR0A4zekdu62sBrp0tJ1Meijopl+0spgIvhu6FDLx6l\n3YIgCILwx2WFZpIDpn1ysBMakLiB5AxOrbtG6du9WE6nLvBFB4019XL2GD88k0LR3iS8/z0BMQnB\nrPHa9aQB/+XxAwq8o0P0jbpIcQdLUsUkcepyPL+/VEMpuVTsNlDhIVA2DDWUUHAxFlNLLX/zlI1f\n7mqgVKnh7F4DLudt+swOVNuK+fRyBC276niz47Y8PGDaxi8PtLHmUBkDyRpGLZ8x5JkJQpw0vljL\nmYQMun6VgvPjBgoOlBH9m2aMUfI5jY04MH3QyvZ9+XS1xGLpNeOaBmlzLj0tKbTvqaM3K5eK3fHA\n/WRvF69Tu95D6W9vU/SDkOzqtJO+Dhfal+Sg1+tx0jPqwn5vkqKZY5QKGO2mscEl1++ZxPvcVop2\nJqHdqKG9tI2inyTI19J2k8NmF63aSMCD8z8UFL9bjVGKxD7QSnZpFdrfNJEYtfRZuOxD9N2MCNm+\nTd9NDV5ANe3BiYErLaVoo+CL96rIe7mZgf79qJfpZ5Qaco8cImWkg+yjn/HS63lEB3a5BxrIrhmk\n6nQ16eucvLWngc1osJQlLX0PPIypL+kHen59nuGjNizA9qxcTlfsQB2aTfbdwVTnoOh0+FMSD9bP\nh3GO3+ZS9TDb95USvUAWemzYDMSSKIllrgVBEIQ/TSs2k7wkdxQpPz0AN5rpH4dI5dPzDlkTFcWa\nvwz8US8y5njahz1kM0afRdWrGbywNuT41RokXRzPPxuB/hkdkk6HpJcz0/iGuXDRj37nVjS+Cezu\nSIxZ0Pdv5pDMbQr/uncL0boEcsoisfx7INM6PYkTWIMCpToWKTmXnB/o5H22m5wBTL/IQ1oXi3Fv\nMUVAX3g2NTmPs8WpSJKBzFfy5DGpUbFoJQPSBtA8G4+kC2Zvl7REnc9v3gpXzbjx01OYz+FuB3hs\nmFDwgk4OolQxCZx4NZXihPAoNgJUiuBfAxl/deKPSMNGz+8CmeOPOiAhNxAER7C9rBTjMz7sVidE\nxAL++3tJTwVzniqEbivjKDiUJ/fV3QmUz8UDQ9inWLqfA42PluLQG+Q+CiZu/fQ3DUJWMbuT44iW\ntvB6bQJ80MvY7IuRi9wDD2MVrAEwT1BxuY5PW/Kho43yK8NzDhv7+CLtJLBnS9iwEZ+D9p/XU1Dd\nTQ+Qvi1+fh32a2yrHibnSDHSkx+TIwiCIAjfiZWdSV7M1CTqtRmY0hsoeO8mVwzhQXAkOa/mIy00\nTjPUKiXakE31plR2b3rw5ljMvZjuyW1YE2GgKGMmMJmEmMjZgMoLcG9SzmoqDVQcSSLzaC2mGrnN\np85Xk7lRg/frSSAWzWwKUIO0AS7YJ4BA2VOwfZtukRbNZKTv/wWrpeqs2JIEnMduv82HZujBTFGM\nE0hBO3P8WgM587KjfohJoeiV3PkVKuPYnQUFHcMUbNLx4UU/r9VvCex0cim/jOMjkJZsQMuXAPc3\nlnneFBAhxgcpfLGePhTkpMfD3WFA90Bjs+cPVfHjnYLtccEfItF/HQ/04PSBcql74AHqnTUN/wXk\nHCrHqNMAqVS82kHm/2PGu9MQKNNBy1EbOUeK52eJlXFU9DdTAVi7a8k8cJjEvreDwfDEIIW5zej3\nFXMiY7H7SxAEQRBWvhUeJCvl//QXiGK8gPHVPMhtoCUBiEqYe4APCHu5SqWMglEHPgIByvhdOZsW\nIQe4XtswX/zBg3ZjEtoFHutbvnLN/WBabseJI8fJkZafISOclFGKJQO84zZ6zlRx8I1rpHXmIWc+\nnfi8BM59AucIGHLmZ8wfhkoVKGdOALVEneooKmJcvPVWM6qdGbw20kvjOQf6fWXBR/luG0NmB6pn\nDeh1kcFiR/2LBoQv/CQD9nxEf7oBE7F0JQaGblivcXxEwZWeJvRqgDtYjcfC4l8FqhgIvzliNsiZ\n0YXqs/72In0xqQx15sv7bW207+qdd9y8fg6xWGDbNzY5+3f3V3eBKNSrlo7ZH4oyCgmwzvls7iHu\nG600oqPnh0u/ZCqtjwW+xDeT8XYPc/gf6nHu3M+V4i1LfVUQBEEQVrwVO9zCbXdgt97FC9hH7jJm\nc8yf11ebSn0ytJuZFxAvRLs5FRikoWMYfE66GpqBJIw6OcC1f9pAQXk9ffYFHuurnoaOXiz3XLgn\nAvtXryc3GQ7/UwPWcQ9MeRi7dY2uG/cxj67PQV/3TcYmPKiiNESvUzAzLEC1/u8owM+b7/finQb7\n9U5OAumbH1NmL0bHdlz0XB/GPe7E61uuzkiMGQr6bjhI+XEWaductJvhpc3rZ4v02j8jr7yBNz/+\nMqwyB7+3O7DbbNhtNqw2x2zgqJJSeA0bBeXd6HdmzWYzVaoowI991AXTLvrrauibdxIKnk+MpO+d\nj7Dec+Iel/tEqdbAaDf9Ng+MmzlT54LADx7V96Ng1IndDbjvYCrpBMJ+eCzUzwDTHrxTHsYmPIAH\n5/jMXMMRGPca4OJ5uc4pG20nBiE5JTie+XFaFUv6PgU9NR1YJ/x475kxveNC/+OEQADvpKXcTFrZ\nvnn1j91oo+v6MGMTHrzjd+hq7QX+CrUSmLrDmbRa2tFRnRvPmM2G1WrDPTWvBYIgCILwJ2GFZpI9\nXP1ZJYdH5a2+o7U0BuZJVqkhmDpTkFaSBzdaZz9TPaVkXuAzY10KPcfMpFUHhziYLh+YfQELVRTg\nYs0CwzSkzAO89uExsl8uITjvcgRpv6jlxBuVZL44OHts0bHjcnHKqNkADQDl0yHbHvqPNlA4uzOS\nU6Y0OdBZpaPYlEd/QROJF5vkMo9Usn1dSFnL/ChQLbVfaaDiWAqZ1bWcAarON7J749J1ajdthYt3\nSXw2AokMeKeXF6SQjLFSDvC1c94SUwBmsnNDZ7EIzlkNsaT9Yyxn3nHwyo9CngRot1Kf1UbpnhJ5\nOybw4yEs6JOyiqm4Wkvmy4PB+ZelFKo2dFOwS359UA+wWv6iNjmPophaMtPy5X0xM20MKXPBfgZ7\nVw1pNcGxxAW5RcH96cXUf/7Ps3USk0DXkdRAm5e6B+6f1+dHFbjG+v3HqRqqJPMf9ss7EzL4dK88\n1MX9eSdniORKZty8Mv7HY+NgdWfIJ7GcOr8f7SrwOoZoBMBG3q7K2SOqTI3s1gde3nPLTxdIf/D2\nC4IgCMIfm+99880333UbmPL973ffiFBTHtxeUKojQl7MekRujzysQP2AswFMefB+vcj3ZlY/i4ic\nFyA+Md9FnYtxe/A+pUC1+sGHsngnPLBI/3rdHlBFPP7zc3twM3dO40fnoau8iIMj8xdh8U64YJUi\n5N5xcclYQtsyC6x4pzyw2D23mKlhCv++lj4grayS+p2PbwEXQRAEQXicViv/4nv3c5wIkgXhz8mU\nB1aLadsEQRCEP1/3GySv2DHJgiA8BBEgC4IgCMJ9EUGyIAiCIAiCIIQRQbIgCIIgCIIghBFBsiAI\ngiAIgiCEEUGyIAiCIAiCIIQRQbIgCIIgCIIghBFBsiAIgiAIgiCEEUHyQnwzSwr/afPes2G9NYzV\nGlwme8xixnJrGMutO/OX+V4p3A4sn5ux3DJjvfct9+OUc871fPI8dJXvR7+jdXY57xletwfvwywb\nPbXE/T+zmMy87wxTaMxHb8zn8Ad3HqJSQRAEQfjjsmKXpe7KL+LgSOhn8rLU9poSSq/CqZa3yZTk\nOWGtHZVkfrSVobM7UNk60e9qCysvuLzw2I0mtpX3Bj6PxdR2HKN2+RXdxgba6PVtJSc59nGc4BM3\ndqOebeWBpbITdjB0NhcVHr74dSulHQ5AwZVPmgLLQz8ZXmsniXvC+iJm/spxD1zuf5p562grfaOg\nX2Z1uQe1XD97rZ1kHujlyifNT/TazTHqZ6AlK3jN7DcpzW2gJ7BZUFZJxSIr4FneLyH7ahpDzTtQ\n2brR72oN2WvA1FaKURsB0w4u/ayS47MriCs49e5xMjcFrsPqeOr/TzWmF4/xhdf32E9REARBEL5t\nKzRIjiDl+HG63Lc5eaAZw5EycgwaNEqwfuUH4ODJXjLPZsiH+ybh3iQA3q99gIL684fQryaQfVOg\nVQLjvWwr7yWnrJKqrCh63qikILeBgf5S1Mu0yDXSzWGfYcUEyc7fDcK+SizFocFTBGmHarHsbkO/\nq5cnv+r0JJDAlcu5KL+W+42nIh8pQAZQ6TI425lKV/5+PnzMJ7FcP6v0ufRczkD7bQXIAcGlrp2Y\nchvoSd7Bp8ezYLiDbT+vRdrYSI4+bCGRqWHeesdFxbsp8jX/fiz1x0p54W/jUSmdXK05RsHPPmKo\nMxeVbwKnOolWUy7SOviitZ7Cn9cgBX5cggLVWh3aDfDFt3rmgiAIgvBkrNjhFmqdDkm/HhWg3RCP\nVheLahXMRrPmVnps/pBvhGaDNeg3xqGV4pCkOCRJh2oV2AeuAUm8vtOAShlLZlk+MEh/oBz71SYK\nd1TSYws+bvbausk27ufgO344V0dh/n70xlqss4+5PfS/X4t+5lH0hd7Zx+Jj15soLKzDVFMp78+v\nwzIebLP7Vielge/pjfu5NBDyGH98mMb8mX2VdN1yhpyfn/66Sk6+3017TYl8zI56xnwwdrUevTGf\n7IvAxToK8/PJLu+e86je+/UiF33BOj30VJfQeD18iIGf/poSznTb5E3bNUp3lNA4sw3gA2J06HW6\nQD/EIek0AFguVHH4fXNIeU7aC0tov+WS939QR7YxpC0D8+tfyNj1JgprroX0QT2FNdfkjWkHXTN9\nEeiPoXtyOcv2s89GY/5+Cv/lNJcu9TI2J5nqx9JRP1tuaV037umQ9ixxDzywKSf9wIniLKJXK4je\nlMWpDdDy+d15h1q7mukjhdxNkfIHaxNIS08iel0k6rVxpKQbYPSO3NbVBl47XUqiPhZ1VCzbX0oB\nXAzfDR168QjtFgRBEIQ/Mis0kxww7ZODndCAxA0kZ3Bq3TVK3+7Fcjp1gS86aKypl7PH+OGZFIr2\nJuH97wmISQhmjdeuJw34L48fUOAdHaJv1EWKO1iSKiaJU5fj+f2lGkrJpWK3gQoPgbJhqKGEgoux\nmFpq+ZunbPxyVwOlSg1n9xpwOW/TZ3ag2lbMp5cjaNlVx5sdt+XhAdM2fnmgjTWHyhhI1jBq+Ywh\nz0wQ4qTxxVrOJGTQ9asUnB83UHCgjOjfNGOMks9pbMSB6YNWtu/Lp6slFkuvGdc0SJtz6WlJoX1P\nHb1ZuVTsjgfuJ3u7eJ3a9R5Kf3uboh+EZFennfR1uNC+JAe9Xo+TnlEX9nuTFM0co1TAaDeNDS65\nfs8k3ue2UrQzCe1GDe2lbRT9JEG+lrabHDa7aNVGAh6c/6Gg+N1qjFIk9oFWskur0P6micSopc/C\nZR+i72ZEyPZt+m5q8AKqaQ9ODFxpKUUbBV+8V0Xey80M9O9HvUw/o9SQe+QQKSMdZB/9jJdezyM6\nsMs90EB2zSBVp6tJX+fkrT0NbEaDpSxp6Xvgkcj3LACrwTJ0B+9eQ7CffXcw1TkoOh3+lMSD9fNh\nnOO3uVQ9zPZ9pUSvml/62LAZiCVREstcC4IgCH+aVmwmeUnuKFJ+egBuNNM/DpHKp+cdsiYqijV/\nGfijXmTM8bQPe8hmjD6LqlczeGFtyPGrNUi6OJ5/NgL9MzoknQ5JL2em8Q1z4aIf/c6taHwT2N2R\nGLOg79/MIZnbFP517xaidQnklEVi+fdApnV6EiewBgVKdSxSci45P9DJ+2w3OQOYfpGHtC4W495i\nioC+8Gxqch5ni1ORJAOZr+QhrQaiYtFKBqQNoHk2HkkXzN4uaYk6n9+8Fa6aceOnpzCfw90O8Ngw\noeAFnRxEqWISOPFqKsUJ4VFsBKgUwb8GAjt14o9Iw0bP7wKZ4486ICE3EARHsL2sFOMzPuxWJ0TE\nAv77e0lPBXOeKoRuK+MoOJQn99XdCZTPxQND2KdYup8DjY+W4tAb5D4KjvLw0980CFnF7E6OI1ra\nwuu1CfBBL2OzL0Yucg88jNUajMDhkx3YJ1xYbzRz0AyMzh0nPPbxRdpJYM+WsGEjPgftP6+noLqb\nHiB9W/z8OuzX2FY9TM6RYqQnPyZHEARBEL4TKzuTvJipSdRrMzClN1Dw3k2uGMKD4EhyXs1HWiBD\nNscqJdqQTfWmVHZvevDmWMy9mO7JbVgTYaAoYyYwmYSYyNmAygtwb1LOaioNVBxJIvNoLaYauc2n\nzleTuVGD9+tJeWdjjQAAIABJREFUIBbNbApQg7QBLtgngEDZU7B9m26RFs1kpO//Baul6qzYkgSc\nx26/zYdm6MFMUYwTSEE7c/xaAznzsqN+iEmh6JXc+RUq49idBQUdwxRs0vHhRT+v1W8J7HRyKb+M\n4yOQlmxAy5cA9zeWOXwKiFDjgxS+WE8fCnLS4+HuMKB7oLHZ84eq+PFOwfa44A+R6L+OB3pw+kC5\n1D3wAPUGaSj4VSnOwnrS/qEbYmLJ2QDt66NCynPQctRGzpHi+VliZRwV/c1UANbuWjIPHCax7+1g\nMDwxSGFuM/p9xZzIWOz+EgRBEISVb4UHyUr5P/4FohgvYHw1D3IbaEkAohLmHuADwl6uUimjYNSB\nj0CAMn5XzqZFyAGu1zbMF3/woN2YhHaBx/qWr1xzP5iW23HiyHFypOVnyAgnZZRiyQDvuI2eM1Uc\nfOMaaZ15yJlPJz4vgXOfwDkChpz5GfOHoVIFypkTQC1RpzqKihgXb73VjGpnBq+N9NJ4zoF+X1nw\nUb7bxpDZgepZA3pdZLDYUf+iAeELP8mAPR/Rn27ARCxdiYGhG9ZrHB9RcKWnCb0a4A5W47Gw+FeB\nKgbCb46YDXJmdKH6rL+9SF9MKkOd+fJ+Wxvtu3rnHTevn0MsFtj2jU3O/t391V0gCvWqpWP2h7Yu\niYrOZip8gFKeCcb5t+uD9d9opREdPT9c+iVTaX0s8CW+mYy3e5jD/1CPc+d+rhRvWeqrgiAIgrDi\nrdjhFm67A7v1Ll7APnKXMZtj/ry+2lTqk6HdzLyAeCHazanAIA0dw+Bz0tXQDCRh1MkBrv3TBgrK\n6+mzL/BYX/U0dPRiuefCPRHYv3o9uclw+J8asI57YMrD2K1rdN24j3l0fQ76um8yNuFBFaUhep2C\nmWEBqvV/RwF+3ny/F+802K93chJI3/yYMnsxOrbjouf6MO5xJ17fcnVGYsxQ0HfDQcqPs0jb5qTd\nDC9tXj9bpNf+GXnlDbz58ZdhlTn4vd2B3WbDbrNhtTlmA0eVlMJr2Cgo70a/M2s2m6lSRQF+7KMu\nmHbRX1dD37yTUPB8YiR973yE9Z4T97jcJ0q1Bka76bd5YNzMmToXBH7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tkOt/A6XTjtw/gA59AwYw7X3Hl9tWnUp0C7lTkB8Xy0m9KAARo67oLfTVdDC5CMSScHuM5PGygo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\nCCGECCBBshBCCCGEEAH+PyhXRvE4zUKiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c268ce9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg \n",
    "map_img = mpimg.imread('result.png') \n",
    "plt.figure(figsize=(12,8))\n",
    "plt.imshow(map_img)\n",
    "plt.axis('off') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 对应的参数\n",
    "```\n",
    "    learning_rate  0.05\n",
    "    batch_size   32\n",
    "    optimizer  rmsprop\n",
    "    max_number_of_steps 1500\n",
    "```\n",
    "\n",
    "### https://www.tinymind.com/executions/fvtd2w9p 执行记录"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 对目前实现的网络进行了多种方式调整,但结果都不太理想,虽然在本地对 cifar10 的训练结果不错,loss可以在1.4左右,但对于 quiz 数据集一直不能调整至收敛,估计网络结构对于200个分类这种较大分类的数据类型需要对网络结构进行调整,让网络能学习更多的特征,目前正在向这个方向调整中"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 关于稠密连接: 由于 densenet 创新式的对于学习到的特征进行重复利用 reuse,而这种 reuse 利用的是网络结构中 \"每一层网络的输出都会被用于后面 '各层网络'的计算\",这样的方式在网络中是基于 denseblock 的复合结构来实现,在该结构内各层连接的数量有 (l(l+1))//2 个,如果以l=50计算,连接数就有1275了,所以就有密集连接的说法;, 在网络内部各层的输出都相当于是对于前面网络输入的特征重复学习,网络的输出包含了前面层学习到的所有特征,这个方式不像传统网络对数据的传输过程有前后依赖,densenet可以有一个全局的概念,这个全局来源每一层,是可以访问全局,当然这个过程也是渐进的,这样就相当于把有用的特征全部在放一个集合中不断的进行学习再学习,最终可能比较重要的特征的就在这个集合中被网络学习到;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 关于growth: 前面说到的 densenet 网络结构设计是基于对特征进行 reuse,而要实现这个目的并减少网络参数和计算量就归功于 growth;在网络的始端,growth 就在限制网络的宽度上起了作用,沿着网络向后至 denseblock层的时候 growth就在保持网络宽度并在特征 channel 维度上进行增长,由开始到末端,网络不断变窄,channel 通道数不断增加,增加的比例就受 growth 控制;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 以下是引用对论文作者采访:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DenseNet 是受什么启发提出来的？\n",
    "  #### DenseNet 的想法很大程度上源于我们去年发表在 ECCV 上的一个叫做随机深度网络（Deep networks with stochastic depth）工作。当时我们提出了一种类似于 Dropout 的方法来改进ResNet。\n",
    "  #### 我们发现在训练过程中的每一步都随机地「扔掉」（drop）一些层，可以显著的提高 ResNet 的泛化性能。这个方法的成功至少带给我们两点启发：\n",
    "\n",
    "    • 首先，它说明了神经网络其实并不一定要是一个递进层级结构，也就是说网络中的某一层可以不仅仅依赖于紧邻的上一层的特征，而可以依赖于更前面层学习的特征。想像一下在随机深度网络中，当第 l 层被扔\n",
    "    掉之后，第 l+1 层就被直接连到了第 l-1 层；当第 2 到了第 l 层都被扔掉之后，第 l+1 层就直接用到了第 1 层的特征。因此，随机深度网络其实可以看成一个具有随机密集连接的 DenseNet。\n",
    "\n",
    "    • 其次，我们在训练的过程中随机扔掉很多层也不会破坏算法的收敛，说明了 ResNet 具有比较明显的冗余性，网络中的每一层都只提取了很少的特征（即所谓的残差）。实际上，我们将训练好的 ResNet 随机\n",
    "    的去掉几层，对网络的预测结果也不会产生太大的影响。既然每一层学习的特征这么少，能不能降低它的计算量来减小冗余呢？\n",
    "\n",
    "#### DenseNet 的设计正是基于以上两点观察。我们让网络中的每一层都直接与其前面层相连，实现特征的重复利用；同时把网络的每一层设计得特别「窄」，即只学习非常少的特征图（最极端情况就是每一层只学习一个特征图），达到降低冗余性的目的。这两点也是 DenseNet 与其他网络最主要的不同。需要强调的是，第一点是第二点的前提，没有密集连接，我们是不可能把网络设计得太窄的，否则训练会出现欠拟合（under-fitting）现象，即使 ResNet 也是如此。"
   ]
  }
 ],
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